



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a      :  str			     =			logging.get_logger(__name__)







def  lowerCAmelCase_	(lowerCAmelCase__:		int  ):





			"""simple docstring"""


			UpperCAmelCase_:		Dict							      =     SwinConfig(
			    embed_dim=1_9_2		,						depths=(2, 2, 1_8, 2)		,						num_heads=(6, 1_2, 2_4, 4_8)		,						window_size=1_2		,						out_features=["""stage2""", """stage3""", """stage4"""]		,						)

			UpperCAmelCase_:		Dict							      =     DetaConfig(
			    backbone_config=lowerCAmelCase__		,						num_queries=9_0_0		,						encoder_ffn_dim=2_0_4_8		,						decoder_ffn_dim=2_0_4_8		,						num_feature_levels=5		,						assign_first_stage=lowerCAmelCase__		,						with_box_refine=lowerCAmelCase__		,						two_stage=lowerCAmelCase__		,						)

			# set labels
			UpperCAmelCase_:		Optional[int]							      =     """huggingface/label-files"""
			if "o365" in model_name:
						UpperCAmelCase_:		Dict							      =     3_6_6
						UpperCAmelCase_:		Optional[int]							      =     """object365-id2label.json"""
			else:
						UpperCAmelCase_:		Optional[Any]							      =     9_1
						UpperCAmelCase_:		Union[str, Any]							      =     """coco-detection-id2label.json"""

			UpperCAmelCase_:		Tuple							      =     num_labels
			UpperCAmelCase_:		Optional[int]							      =     json.load(open(cached_download(hf_hub_url(lowerCAmelCase__		,						lowerCAmelCase__		,						repo_type="""dataset"""  )  )		,						"""r"""  )  )
			UpperCAmelCase_:		Optional[Any]							      =     {int(lowerCAmelCase__  ): v for k, v in idalabel.items()}
			UpperCAmelCase_:		Any							      =     idalabel
			UpperCAmelCase_:		int							      =     {v: k for k, v in idalabel.items()}

			return config







def  lowerCAmelCase_	(lowerCAmelCase__:		List[str]  ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     []

			# stem
			# fmt: off
			rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")  )
			rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")  )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")  )
			rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")  )
			# stages
			for i in range(len(config.backbone_config.depths  )  ):
						for j in range(config.backbone_config.depths[i]  ):
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.norm2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias', F'model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias')  )

						if i < 3:
									rename_keys.append((F'backbone.0.body.layers.{i}.downsample.reduction.weight', F'model.backbone.model.encoder.layers.{i}.downsample.reduction.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.weight', F'model.backbone.model.encoder.layers.{i}.downsample.norm.weight')  )
									rename_keys.append((F'backbone.0.body.layers.{i}.downsample.norm.bias', F'model.backbone.model.encoder.layers.{i}.downsample.norm.bias')  )

			rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")  )
			rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")  )
			rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")  )
			rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")  )
			rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")  )
			rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")  )

			# transformer encoder
			for i in range(config.encoder_layers  ):
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight', F'model.encoder.layers.{i}.self_attn.sampling_offsets.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias', F'model.encoder.layers.{i}.self_attn.sampling_offsets.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.weight', F'model.encoder.layers.{i}.self_attn.attention_weights.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.attention_weights.bias', F'model.encoder.layers.{i}.self_attn.attention_weights.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.weight', F'model.encoder.layers.{i}.self_attn.value_proj.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.value_proj.bias', F'model.encoder.layers.{i}.self_attn.value_proj.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.weight', F'model.encoder.layers.{i}.self_attn.output_proj.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.self_attn.output_proj.bias', F'model.encoder.layers.{i}.self_attn.output_proj.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm1.weight', F'model.encoder.layers.{i}.self_attn_layer_norm.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm1.bias', F'model.encoder.layers.{i}.self_attn_layer_norm.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear1.weight', F'model.encoder.layers.{i}.fc1.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear1.bias', F'model.encoder.layers.{i}.fc1.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear2.weight', F'model.encoder.layers.{i}.fc2.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.linear2.bias', F'model.encoder.layers.{i}.fc2.bias')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm2.weight', F'model.encoder.layers.{i}.final_layer_norm.weight')  )
						rename_keys.append((F'transformer.encoder.layers.{i}.norm2.bias', F'model.encoder.layers.{i}.final_layer_norm.bias')  )

			# transformer decoder
			for i in range(config.decoder_layers  ):
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias', F'model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.weight', F'model.decoder.layers.{i}.encoder_attn.attention_weights.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.attention_weights.bias', F'model.decoder.layers.{i}.encoder_attn.attention_weights.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.weight', F'model.decoder.layers.{i}.encoder_attn.value_proj.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.value_proj.bias', F'model.decoder.layers.{i}.encoder_attn.value_proj.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.weight', F'model.decoder.layers.{i}.encoder_attn.output_proj.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.cross_attn.output_proj.bias', F'model.decoder.layers.{i}.encoder_attn.output_proj.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm1.weight', F'model.decoder.layers.{i}.encoder_attn_layer_norm.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm1.bias', F'model.decoder.layers.{i}.encoder_attn_layer_norm.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.weight', F'model.decoder.layers.{i}.self_attn.out_proj.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.self_attn.out_proj.bias', F'model.decoder.layers.{i}.self_attn.out_proj.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm2.weight', F'model.decoder.layers.{i}.self_attn_layer_norm.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm2.bias', F'model.decoder.layers.{i}.self_attn_layer_norm.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear1.weight', F'model.decoder.layers.{i}.fc1.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear1.bias', F'model.decoder.layers.{i}.fc1.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear2.weight', F'model.decoder.layers.{i}.fc2.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.linear2.bias', F'model.decoder.layers.{i}.fc2.bias')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm3.weight', F'model.decoder.layers.{i}.final_layer_norm.weight')  )
						rename_keys.append((F'transformer.decoder.layers.{i}.norm3.bias', F'model.decoder.layers.{i}.final_layer_norm.bias')  )

			# fmt: on

			return rename_keys







def  lowerCAmelCase_	(lowerCAmelCase__:		Tuple		,						lowerCAmelCase__:		Optional[Any]		,						lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			UpperCAmelCase_:		Any							      =     dct.pop(lowerCAmelCase__  )
			UpperCAmelCase_:		Union[str, Any]							      =     val







def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
			for i in range(len(backbone_config.depths  )  ):
						UpperCAmelCase_:		Union[str, Any]							      =     num_features[i]
						for j in range(backbone_config.depths[i]  ):
									# fmt: off
									# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
									UpperCAmelCase_:		Optional[Any]							      =     state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight'  )
									UpperCAmelCase_:		str							      =     state_dict.pop(F'backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias'  )
									# next, add query, keys and values (in that order) to the state dict
									UpperCAmelCase_:		Any							      =     in_proj_weight[:dim, :]
									UpperCAmelCase_:		List[str]							      =     in_proj_bias[: dim]
									UpperCAmelCase_:		Tuple							      =     in_proj_weight[
									    dim : dim * 2, :
									]
									UpperCAmelCase_:		List[str]							      =     in_proj_bias[
									    dim : dim * 2
									]
									UpperCAmelCase_:		Union[str, Any]							      =     in_proj_weight[
									    -dim :, :
									]
									UpperCAmelCase_:		Optional[int]							      =     in_proj_bias[-dim :]
									# fmt: on







def  lowerCAmelCase_	(lowerCAmelCase__:		str		,						lowerCAmelCase__:		List[Any]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[Any]							      =     config.d_model
			for i in range(config.decoder_layers  ):
						# read in weights + bias of input projection layer of self-attention
						UpperCAmelCase_:		Dict							      =     state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
						UpperCAmelCase_:		Dict							      =     state_dict.pop(F'transformer.decoder.layers.{i}.self_attn.in_proj_bias'  )
						# next, add query, keys and values (in that order) to the state dict
						UpperCAmelCase_:		str							      =     in_proj_weight[:hidden_size, :]
						UpperCAmelCase_:		Union[str, Any]							      =     in_proj_bias[:hidden_size]
						UpperCAmelCase_:		str							      =     in_proj_weight[
						    hidden_size : hidden_size * 2, :
						]
						UpperCAmelCase_:		Optional[Any]							      =     in_proj_bias[hidden_size : hidden_size * 2]
						UpperCAmelCase_:		int							      =     in_proj_weight[-hidden_size:, :]
						UpperCAmelCase_:		List[Any]							      =     in_proj_bias[-hidden_size:]







def  lowerCAmelCase_	():





			"""simple docstring"""


			UpperCAmelCase_:		Optional[Any]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			UpperCAmelCase_:		Union[str, Any]							      =     Image.open(requests.get(lowerCAmelCase__		,						stream=lowerCAmelCase__  ).raw  )

			return im







@torch.no_grad()
def  lowerCAmelCase_	(lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		List[str]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     get_deta_config(lowerCAmelCase__  )

			# load original state dict
			if model_name == "deta-swin-large":
						UpperCAmelCase_:		Tuple							      =     hf_hub_download(repo_id="""nielsr/deta-checkpoints"""		,						filename="""adet_swin_ft.pth"""  )
			elif model_name == "deta-swin-large-o365":
						UpperCAmelCase_:		Optional[Any]							      =     hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365"""		,						filename="""deta_swin_pt_o365.pth"""  )
			else:
						raise ValueError(F'Model name {model_name} not supported'  )

			UpperCAmelCase_:		Optional[Any]							      =     torch.load(lowerCAmelCase__		,						map_location="""cpu"""  )["""model"""]

			# original state dict
			for name, param in state_dict.items():
						print(lowerCAmelCase__		,						param.shape  )

			# rename keys
			UpperCAmelCase_:		List[str]							      =     create_rename_keys(lowerCAmelCase__  )
			for src, dest in rename_keys:
						rename_key(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			read_in_swin_q_k_v(lowerCAmelCase__		,						config.backbone_config  )
			read_in_decoder_q_k_v(lowerCAmelCase__		,						lowerCAmelCase__  )

			# fix some prefixes
			for key in state_dict.copy().keys():
						if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
									UpperCAmelCase_:		Tuple							      =     state_dict.pop(lowerCAmelCase__  )
									UpperCAmelCase_:		int							      =     val
						if "input_proj" in key:
									UpperCAmelCase_:		Optional[Any]							      =     state_dict.pop(lowerCAmelCase__  )
									UpperCAmelCase_:		Any							      =     val
						if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
									UpperCAmelCase_:		Tuple							      =     state_dict.pop(lowerCAmelCase__  )
									UpperCAmelCase_:		Dict							      =     val

    # finally, create HuggingFace model and load state dict
			UpperCAmelCase_:		List[str]							      =     DetaForObjectDetection(lowerCAmelCase__  )
			model.load_state_dict(lowerCAmelCase__  )
			model.eval()

			UpperCAmelCase_:		Optional[int]							      =     """cuda""" if torch.cuda.is_available() else """cpu"""
			model.to(lowerCAmelCase__  )

			# load image processor
			UpperCAmelCase_:		Dict							      =     DetaImageProcessor(format="""coco_detection"""  )

			# verify our conversion on image
			UpperCAmelCase_:		Dict							      =     prepare_img()
			UpperCAmelCase_:		Union[str, Any]							      =     processor(images=lowerCAmelCase__		,						return_tensors="""pt"""  )
			UpperCAmelCase_:		List[str]							      =     encoding["""pixel_values"""]
			UpperCAmelCase_:		str							      =     model(pixel_values.to(lowerCAmelCase__  )  )

			# verify logits
			print("""Logits:"""		,						outputs.logits[0, :3, :3]  )
			print("""Boxes:"""		,						outputs.pred_boxes[0, :3, :3]  )
			if model_name == "deta-swin-large":
						UpperCAmelCase_:		List[Any]							      =     torch.tensor(
						    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
						UpperCAmelCase_:		int							      =     torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
			elif model_name == "deta-swin-large-o365":
						UpperCAmelCase_:		List[str]							      =     torch.tensor(
						    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
						UpperCAmelCase_:		Dict							      =     torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
			assert torch.allclose(outputs.logits[0, :3, :3]		,						expected_logits.to(lowerCAmelCase__  )		,						atol=1e-4  )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3]		,						expected_boxes.to(lowerCAmelCase__  )		,						atol=1e-4  )
			print("""Everything ok!"""  )

			if pytorch_dump_folder_path:
						# Save model and processor
						logger.info(F'Saving PyTorch model and processor to {pytorch_dump_folder_path}...'  )
						Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )
						model.save_pretrained(lowerCAmelCase__  )
						processor.save_pretrained(lowerCAmelCase__  )

			# Push to hub
			if push_to_hub:
						print("""Pushing model and processor to hub..."""  )
						model.push_to_hub(F'jozhang97/{model_name}'  )
						processor.push_to_hub(F'jozhang97/{model_name}'  )


if __name__ == "__main__":
	a      :  int			     =			argparse.ArgumentParser()

	parser.add_argument(
	    '--model_name',
	    type=str,
	    default='deta-swin-large',
	    choices=['deta-swin-large', 'deta-swin-large-o365'],
	    help='Name of the model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default=None,
	    type=str,
	    help='Path to the folder to output PyTorch model.',
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)
	a      :  Tuple			     =			parser.parse_args()
	convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











from __future__ import annotations







def  lowerCAmelCase_	(lowerCAmelCase__:		int | str  ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]							      =     str(lowerCAmelCase__  )
			return n == n[::-1]







def  lowerCAmelCase_	(lowerCAmelCase__:		int = 1_0_0_0_0_0_0  ):





			"""simple docstring"""


			UpperCAmelCase_:		int							      =     0

			for i in range(1		,						lowerCAmelCase__  ):
						if is_palindrome(lowerCAmelCase__  ) and is_palindrome(bin(lowerCAmelCase__  ).split("""b"""  )[1]  ):
									total += i
			return total


if __name__ == "__main__":
	print(solution(int(str(input().strip()))))






